Knowledge and control of aeraulic flows concern one skilled in the art when he seeks to control environmental parameters for purposes of comfort and safety. To attain this objective, two approaches are possible: practical experience or mathematical calculation using recognized physical laws. Traditionally, the two approaches are considered to be competitors, and, most often, the person on the spot will prefer experience when the scientific person will favor calculation. However, neither of these two approaches can claim the quality of absolute precision when it comes to representing an aeraulic flow.
Experimental simulation can only provide a vision of the reality of the flows remaining dependant upon conditions of experience (nature of the tracer, processes for releasing tracer into the environment, aeraulic and operational conditions in the environment being considered at the time of the experiment). Also, if experimental simulation can provide advantageous teaching on the qualitative representation of aeraulic flows, it remains linked to the conditions of experience and should not be extrapolated to other conditions. Moreover, the margin of error of experimental simulation in the case of isolated quantitative study remains very significant and can often be underestimated by the experimenters.
Also, in an application including the concept of risk management, which, by definition, requires the extrapolation of results and their projections in different systems of operational configurations, only the modeling by calculation is theoretically capable of meeting, in an objective manner, requirements of the problem posed. This approach by calculation is known to one skilled in the art under the acronym CFD (CFD: Computational Fluid Dynamics) and relies on the search for digital solutions with approximations of equations of state of fluid dynamics.
In practice, the CFD proceeds according to a principle that is always identical regardless of the method that is implemented. Actually, the known methods all rely on the following stages:                1. Selection of the Equations of State of Fluid Dynamics Adapted to the Problem that is to be Solved        
The description of the flow of a fluid in movement is controlled by the equations of state, widely known under the name Navier-Stokes equations, to which are added restrictions such as:                Newtonian and incompressible fluid        Constant physical properties        Buoyancy effect controlled by the Boussinesq approximation        Ignored viscous dissipation        Ignored radiation        One-phase fluid        One-component fluid.        
These hypotheses and restrictions make it possible, however, to cover a very extensive application.
Under these conditions, the equations of state are written:                Law of conservation of mass∇·{right arrow over (V)}=0        Law of conservation of the quantity of movement        
                    ∂                  V          →                            ∂        t              +                  V        →            ⁡              (                  ∇                      ·                          V              →                                      )              =                    -                  1          p                    ⁢              ∇        P              +          v      ⁢                        ∇          2                ⁢                  V          →                      +                  g        →            ⁢                          ⁢              β        ⁡                  (                      T            -                          T              0                                )                      +          F      int                      Law of Conservation of Energy:        
                    ∂        T                    ∂        t              +                  V        →            ⁡              (                  ∇                      ·            T                          )              =                    k                  ρ          ⁢                                          ⁢                      C            p                              ⁢                        ∇          2                ⁢        T              +                            S          T                          ρ          ⁢                                          ⁢                      C            p                              .      
The solutions to these equations provide the desired information on the flow variables of the fluid (also called primitive variables), i.e.: the velocity V={u, v, w}, the pressure P, and the temperature T.                2. Linearization of the Equations of State with Provision of Approximation Models        
The mathematical expression of the law of conservation of the quantity of movement reveals a non-linear term that makes the equation impossible to solve in an exact manner. This impossibility is known under the name of “Closure Problem.” Consequently, it is necessary to approximate the solution. Numerous models have been proposed with more or less success. Among the latter, the model k-ε is certainly the most commonly used.
The model k-ε is based on two equations that take into account the turbulent kinetic energy (k) and the dissipation of the turbulence (ε). In this model, the turbulent viscosity is determined in an empirical manner by the equation:
      v    t    =            C      μ        ⁢                  k        2            ɛ      an equation in which Cμ is a constant (generally 0.09).
In fact, the implementation of the model k-ε requires the introduction of several other empirical functions such as Fm, F1, F2 and E, whose values vary according to the authors.
Consequently, it clearly appears that the conventional methods for modeling aeraulic flows cannot provide exact solutions to the problem posed, and the relevance of the approximated values is neither always verifiable nor even adjustable by the user.                3. Discretization of the Calculation Area for Generating Algebraic Systems that are Capable of Providing—by an Iterative Method—Solutions that are Close to the Equations of these Systems        
The foundation of the modeling by calculation then relies on a principle of discretization of space and indexing of equations of state modified by the introduction of empirical values. The calculation area is meshed into a finite number of volumes called mesh M whose sum provides the entirety of this range. In each of these meshes, the scalar variables P and T are evaluated.
In addition, three complementary networks offset in the directions of the individual trihedron {u, v, w} are attached for the determination of the velocity vector V={u, v, w} along each of the axes. The integration of the partial differential equations in their respective mesh is then obtained by the determination of the term “convection-diffusion.”
The algebraic equation can then be resolved at point A of the mesh M for knowing the value of the calculated variable.
It is useful to note that, to be solved, this equation requires knowledge of values of the calculated variables in the adjacent meshes. This interdependence of all of the meshes for building the final result in the calculation area requires the implementation of an iterative process for refining the values in each volume based on values calculated in the adjacent cells until stabilized general solutions are obtained.                4. Transcription of the Iterative Process in Algorithms that can be Treated by a Computerized System        
The search for convergence of variables in the discretized range requires the use of computerized systems with considerable calculating powers. Actually, the precision of the result that is obtained is conditioned by the size of the mesh: the smaller the latter is, the more precise the result. However, covering the range by small-size meshes has the consequence of increasing the number thereof and therefore of increasing the calculating time necessary for obtaining the result (it should be noted that the increase in the number of meshes is also conditioned by the equipment upon which the calculation is performed and in particular by the size of the memory available for the calculation).
Most of the algorithms making possible this iterative calculation have been developed from the 1960s to the 1980s and have known only a few improvements until today. The most used algorithms in the commercial codes are known under the names of SIMPLE (SIMPLE: Semi-Implicit Method for Pressure Linked Equations), SIMPLER, MAC, and SOLA, the foregoing being a non-exhaustive list.                5. Introduction of Initial Conditions and Boundary Conditions Satisfying Both Requirements of Algorithms and of the Problem Posed        
Regardless of the algorithm that is used, the implementation requires the introduction of data making it possible to “turn around” the calculation. These data consist of initial conditions and boundary conditions of the calculation area. In the conventional approach of the CFD codes, the algorithm is deployed from meshes in which the values of the primitive variables (V, P, T) are known and are diffused toward all of the adjacent meshes while having to satisfy the boundary conditions defined at the boundaries of this range.
In conventional CFD, the boundary conditions pose the value of the velocity module at the boundary of the range (in general, zero velocity) and develop toward the interior of the range according to the models of Dirichlet or Neumann.                6. System Resolution        
The algorithm of a conventional CFD code is then activated until the time when the convergence conditions (or stability of the solution) are satisfied. However, the conventional CFD codes do not use means that are capable of assessing the relevance of the calculated solution. This means that the relevance of the result of the calculation is primarily conditioned by the CFD expert during the selection of the most suitable code for the modeling type to be carried out and, for a given code, by the selection of models and values of the variables as well as by the introduction of the initial conditions.
If this conditionality on the human factor of the calculated solution does not pose a problem in the structures tested in modeling by calculation (in the field of aeronautics, for example), it nevertheless constitutes an obstacle for a routine use by a person on the spot (such as, for example, for the modeling of aeraulic flows in a laboratory) with, as a corollary, the problems of misuse or management of interfaces cited above.
The conventional principle of modeling by calculation of aeraulic flows therefore further promotes CFD codes for applications with high added value and with restricted parametric variability (as in the field of aeronautics) as well as for real configurations (laboratories, public site, . . . ) prevailing within a framework for evaluation of the risk of airborne contamination.
However, the principle presented in detail above is adopted by almost all of the current codes, whether they are made available to the public or kept strictly in house for the organizations that hold the rights thereto.